EstateML : Housing Price Prediction

This project builds a complete machine learning pipeline to predict median house values in California using a housing dataset. It includes exploratory data analysis, preprocessing with pipelines, model training using RandomForestRegressor, and prediction output saved as CSV.

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📁 Project File Overview

File Name	Purpose
01_Analyzing_the_data.ipynb	Explore and understand data distribution, summary, and statistics.
02_Find_best_ ML Algorithm.ipynb	Compares ML models like Linear Regression, Decision Trees, etc.
03_Visualizing_the_data.ipynb	Data visualization to understand trends and correlations.
04_ML_(Final Part).ipynb	Final pipeline creation and model saving.
ML(For - User).py	Runs model training or prediction. Automatically manages input/output.
housing.csv	Original training dataset.
input.csv	Test input generated from test split.
output.csv	Predictions generated from input.csv.

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🔍 Features

• Full machine learning pipeline using Scikit-learn
• Handles missing values using SimpleImputer
• Encodes categorical column ocean_proximity using OneHotEncoder
• Feature scaling with StandardScaler
• Uses ColumnTransformer and Pipeline
• Saves trained model and pipeline as .pkl files
• Stores input and prediction results as CSV files

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✅ Requirements

Install required Python packages:

pip install pandas numpy scikit-learn joblib

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▶️ How to Run the Project

1. Make sure housing.csv is present in the project folder.

2. Open terminal or command prompt in the project directory.

3. Run the main script:

python ML(For - User).py

What Happens When You Run the Script?

If model.pkl and pipeline.pkl do NOT exist:

Splits the dataset

Preprocesses training and test data

Trains the model

Saves model and pipeline

Exports test data to input.csv

If model.pkl and pipeline.pkl already exist:

Loads input.csv

Transforms test data using saved pipeline

Makes predictions

Saves results in output.csv

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📦 Files Not Included in GitHub

Files like model.pkl and pipeline.pkl may not be included due to size limits. You can regenerate them by simply running the script.

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📤 Output

After prediction:

output.csv contains:

All input columns from input.csv

A new column median_house_value with predicted house prices

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👨‍💻 Author

Debashish Parida GitHub: https://github.com/debashish-5

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